In 1935, Austrian physicist Erwin Schrödinger described a now famous thought experiment where:

“A cat, a flask containing poison, a tiny bit of radioactive substance and a Geiger counter are placed into a sealed box for one hour. If the Geiger counter doesn't detect radiation, then nothing happens and the cat lives. However if radiation is detected, then the flask is shattered, releasing the poison which kills the cat. According to the Copenhagen interpretation of quantum mechanics, until the box is opened, the cat is simultaneously alive and dead. Yet, once you open the box, the cat will either be alive or dead, not a mixture of alive and dead.”

This was only a thought experiment. Therefore, no actual cat was harmed.

This paradox of quantum physics, known as Schrödinger's Cat, poses the question:

“When does a quantum system stop existing as a mixture of states and become one or the other?”

Unfortunately, data quality projects are not thought experiments. They are complex, time consuming and expensive enterprise initiatives. Typically, a data quality tool is purchased, expert consultants are hired to supplement staffing, production data is copied to a development server and the project begins. Until it is completed and the new system goes live, the project is a potential success or failure. Yet, once the new system starts being used, the project will become either a success or failure.

This paradox, which I refer to as Schrödinger's Data Quality, poses the question:

“When does a data quality project stop existing as potential success or failure and become one or the other?”

Data quality projects should begin with the parallel and complementary efforts of drafting the business requirements while also performing a data quality assessment, which can help you: